Optical Coherence Tomography (hereinafter referred to as “OCT”) is emerging as a dominant medical imaging modality for diagnostic ophthalmology. Optical coherence tomography works similarly to ultrasound tomography, however substituting use of light waves instead of sound waves to reconstruct images of tissue layers based on the reflection of light from tissue interfaces. By using the time-delay, and/or magnitude variance information contained in the light waves which have been reflected from different depths inside a tissue sample, an OCT system can reconstruct a depth-profile of the sample structure. Three-dimensional images can then be created by scanning the light beam laterally across the sample surface to create a 3D tomographic grid. Conventional OCT scanning schemes suffer from a number of drawbacks. Morphometric analysis and clinical applications typically demand high resolution OCT images, which necessitate dense sampling, leading to long scan times. During such long scan times, a person subject to the OCT scan is often required to remain still, sometimes for up to ten seconds, with a fixed gaze on a point without blinking—a challenging feat even for the most determined and those without eye health issues. Long scan times may inevitably increase the likelihood of image corruption from motion artifacts, such as image blurring and ghosting resulting from the subject's eye blinking during an OCT scan session. Although some OCT systems may implement motion tracking features in an attempt to reduce the potential effect of motion artifacts in OCT images, such motion tracking systems typically do not reduce OCT scan times, and may in fact increase scan durations. Additionally, some subjects with eye problems more likely to require OCT scans may be unable to fixate their gaze or eye focus for long OCT scan sessions which may be required by conventional OCT image acquisition. An outstanding need therefore exists for an improved system and method for OCT image acquisition for the benefits of reduced scan times, without significantly comprising the quality of the image acquired from the subject of interest.
Compressive sampling, or compressive sensing, is a technique for signal acquisition and reconstruction utilizing the prior knowledge that the sampled signal is sparse or compressible in nature. Conventional acquisition and reconstruction of images from frequency data using compressive sampling techniques typically follows the basic principle of the Nyquist density sampling theory, which states that to reconstruct an image, the number of Fourier samples that need to be acquired must match the desired resolution, and by extension, the number of pixels of the image. Compressive sampling suggests the possibility of new data acquisition protocols that show that super-resolved signals and images may be reconstructed from far fewer data or measurements than that which was considered necessary under the Nyquist sampling theory. An overview of compressive sampling may be found, for example, in E. J. Candes, J. Romberg, and T. Tao, Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information, IEEE Trans. Inform. Theory, 52, 489-509 (2004); and E. J. Candes and D. L. Donoho, New Tight Frames of Curvelets and Optimal Representations of Objects with Piecewise-C2 Singularities, Communications on Pure and Applied Mathematics, 57, no. 2, 219-266 (2004).